Neil Bruce @NeilBruce0
Professor at the University of Guelph. Research interests include AI, deep learning, neuroscience, eye movements, vision and attention. socs.uoguelph.ca/~brucen/ Guelph, Ontario Joined April 2012-
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@CSProfKGD @ylecun Mine really surprised me.
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@CSProfKGD I’m pretty sure this is what I’ve been doing all along
@TmlrOrg @CSProfKGD And clear communication is the obvious next step. But I think another step that comes soon is a different publishing ecosystem where even errors that are costly to detect can be discovered and purged. Maybe with living documents, provenance and a ledger.
@TmlrOrg @CSProfKGD When it comes to “AI slop” there is an instinct to look for the AI and the kinds of errors it introduces and punish this. The reality is that some obvious AI errors will be the easiest things to detect and repair over time. The “slop” is the real problem.
Tying the definition of "borderline" to scores when the handbook says "Begin by carefully reviewing the reviews. Don't focus too much on the scores. Instead, look carefully at the comments. Judge the quality of the review rather..."? #NeurIPS2026 😑
And another #NeurIPS2026 thing: In email from PC "Pay special attention to borderline papers (e.g., average score 3.5–4.5)" In what bizarro world is 4.5 borderline? Last year all papers I accepted had avg 4. This year highest I have in my pool is 4.25. @NeurIPSConf
On that last point about sharing knowledge with others, I just wanted to double-check with the community that sharing knowledge is still the status quo since what we share could conceivably help others achieve their goals and become our competitors - thoughts? 5/5
Also, since the LLMs have all read my papers (the prompt injections make it through to the reviews for those who couldn't afford more expensive LLMs) - I plan to release all of my submissions over the coming week so that humans can read them too 4/k
The #NeurIPS review process this year is very eye-opening. Reviewers seem disengaged (across all papers - maybe they are tired from being forced to battle LLMs?) 1/k
Just realized I’m running something load bearing that I forgot to smoke test. 😱
@roydanroy Are the penalties for human slop the same?
@gabriberton Let’s keep in mind, Schmidhuber anticipated the non-mystical version: intelligence = compression/prediction. Text, images and audio are different projections of the same world, so scale should push models toward the shortest shared latent structure. PRH is Plato meets AIT. </s>?
Is it conceivable that AGI eventually appears not in large data centres owned by massive corporations, but rather, within a global network of connected collaborating agents? 🤔
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